Academic Search Skill

Overview

This skill provides strategies and tool usage patterns for effectively searching academic literature. It focuses on precision searching to find high-quality, relevant papers.

Search Strategies

1. Keyword Expansion

Start with a broad topic and generate synonyms.

• Topic: "Large Language Models in Healthcare"
• Keywords: "LLM", "Generative AI", "NLP", "Clinical Decision Support", "Electronic Health Records"

2. Boolean Operators

Use standard operators to refine searches:

• AND: narrow results (e.g., LLM AND "clinical trial")
• OR: broaden results (e.g., cancer OR oncology)
• NOT: exclude specific terms (e.g., virus NOT "computer virus")

3. Database Specifics

ArXiv (Computer Science, Physics)

• Best for: Preprints, cutting-edge AI research.
• Search Tip: Use category filters (e.g., cat:cs.AI, cat:cs.CL).
• Sorting: Sort by "Submitted Date" to see the absolute latest work.

Semantic Scholar (General Science)

• Best for: Finding connected papers, traversing citation graphs.
• Search Tip: Use "Highly Influential Citations" to find seminal papers.
• Filtering: Filter by "Has PDF" to ensure full-text access.

GitHub (Open Source Code)

• Best for: Finding implementation code, frameworks, tools.
• Search Tip: Include "implementation", "code", or specific languages (e.g., LLM language:python).
• Sorting: Sort by "stars" to find the most popular repositories.

Usage

Use the provided Python scripts to fetch data.

1. ArXiv Fetcher

python .research-agent/skills/academic-search/scripts/arxiv_fetcher.py --query "Machine Learning" --max_results 5

2. Semantic Scholar Fetcher

python .research-agent/skills/academic-search/scripts/scholar_fetcher.py --query "Transformer Architecture" --limit 5

3. GitHub Fetcher

python .research-agent/skills/academic-search/scripts/github_fetcher.py --query "Research Agent" --limit 5 --sort stars

Verification

• Citation Count: High citations usually indicate impact.
• Venue: Check the conference (NeurIPS, ICML) or journal impact factor.